Do Forecasting Algorithms Need a Crisis-Mode? Machine Learning Based Sales Forecasting in Times of COVID-19

Tobias Fahse ()
Additional contact information
Tobias Fahse: University of St. Gallen

A chapter in Technologies for Digital Transformation, 2024, pp 49-64 from Springer

Abstract: Abstract On average, algorithmic forecasts outperform human forecasts by 10%. This disparity increases further thanks to cutting-edge machine learning (ML) algorithms. As business decisions based on sales forecasting are regarded as particularly important and a variety of other activities rely on them, accurate sales forecasting is critical to companies’ profitability. At the same time, being able to predict the next day’s sales more accurately can significantly reduce food waste and help fulfilling sustainability. Thus, sales forecasting is one of the primary value propositions of artificial intelligence (AI). However, it is crucial for the acceptance and adoption of ML-based sales forecasting algorithms to perform reliably during pandemics such as the covid-19 pandemic. Although governments’ containment measures highly impact the sales of a bakery’s products, no study has yet scrutinized incorporating the stringency of containment measures as an input variable for sales forecasting. Hence, this paper examines the performance of a ML sales forecasting system for baked goods in times of covid-19 and proposes incorporating a covid containment measurement stringency index as an additional input variable to increase forecast accuracy in times of pandemics. This way, prediction accuracy increases by 4.61% on average. Consequently, a containment measures stringency variable should be used to increase accuracy in future pandemics. By simulating an upcoming pandemic, it is further demonstrated how learnings from the covid-19 pandemic could be meaningfully transferred. For this study, real data is used: A Swiss bakery chain provides real sales data covering 5 years including 2 years of the covid-19 pandemic.

Keywords: Sales forecasting; Machine learning; COVID-19; Containment measures (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-52120-1_4

Ordering information: This item can be ordered from
http://www.springer.com/9783031521201

DOI: 10.1007/978-3-031-52120-1_4

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().